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Abstract

Recent advances in low power integrated circuit devices,
micro-electro-mechanical system (MEMS) technologies, and
communications technologies have made possible the deployment of
low-cost, low power sensors that can be integrated to form wireless
sensor networks (WSN). These wireless sensor networks have vast
important applications, i.e.: from battlefield surveillance system
to modern highway and industry monitoring system; from the emergency
rescue system to early forest fire detection and the very
sophisticated earthquake early detection system. Having the broad
range of applications, the sensor network is becoming an integral
part of human lives. However, the success of sensor networks
deployment depends on the reliability of the network itself. There
are many challenging problems to make the deployed network more
reliable. These problems include but not limited to extending
network lifetime, increasing each sensor node throughput, efficient
collection of information, enforcing nodes to collaboratively
accomplish certain network tasks, etc. One important aspect in
designing the algorithm is that the algorithm should be completely
distributed and scalable. This aspect has posed a tremendous
challenge in designing optimal algorithm in sensor networks.
This thesis addresses various challenging issues encountered in
wireless sensor networks. The most important characteristic in
sensor networks is to prolong the network lifetime. However, due to
the stringent energy requirement, the network requires highly energy
efficient resource allocation. This highly energy-efficient resource
allocation requires the application of an energy awareness system.
In fact, we envision a broader resource and environment aware
optimization in the sensor networks. This framework reconfigures the
parameters from different communication layers according to its
environment and resource. We first investigate the application of
online reinforcement learning in solving the modulation and transmit
power selection. We analyze the effectiveness of the learning
algorithm by comparing the effective good throughput that is
successfully delivered per unit energy as a metric. This metric
shows how efficient the energy usage in sensor communication is. In
many practical sensor scenarios, maximizing the energy efficient in
a single sensor node may not be sufficient. Therefore, we continue
to work on the routing problem to maximize the number of delivered
packet before the network becomes useless. The useless network is
characterized by the disintegrated remaining network. We design a
class of energy efficient routing algorithms that explicitly takes
the connectivity condition of the remaining network in to account.
We also present the distributed asynchronous routing implementation
based on reinforcement learning algorithm. This work can be viewed
as distributed connectivity-aware energy efficient routing. We then
explore the advantages obtained by doing cooperative routing for
network lifetime maximization. We propose a power allocation in the
cooperative routing called the maximum lifetime power allocation.
The proposed allocation takes into account the residual energy in
the nodes when doing the cooperation. In fact, our criterion lets
the nodes with more energy to help more compared to the nodes with
less energy. We continue to look at the problem of cooperation
enforcement in ad-hoc network. We show that by combining the
repeated game and self learning algorithm, a better cooperation
point can be obtained. Finally, we demonstrate an example of
channel-aware application for multimedia communication. In all case
studies, we employ optimization scheme that is equipped with the
resource and environment awareness. We hope that the proposed
resource and environment aware optimization framework will serve as
the first step towards the realization of intelligent sensor
communications.